Overview

Dataset statistics

Number of variables22
Number of observations1000
Missing cells500
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory165.2 KiB
Average record size in memory169.1 B

Variable types

Numeric18
Categorical3
Boolean1

Alerts

F3 is highly overall correlated with F14High correlation
F14 is highly overall correlated with F1High correlation
F19 is highly overall correlated with F17High correlation
F20 is highly overall correlated with F19High correlation
F21 is highly overall correlated with ClassHigh correlation
Class is highly overall correlated with F21High correlation
F1 is highly overall correlated with F14High correlation
F7 is highly overall correlated with F15High correlation
F15 is highly overall correlated with F7High correlation
F17 is highly overall correlated with F19High correlation
F21 has 500 (50.0%) missing valuesMissing
F17 is highly skewed (γ1 = 30.53867041)Skewed

Reproduction

Analysis started2023-01-11 12:08:24.021141
Analysis finished2023-01-11 12:08:42.291091
Duration18.27 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

F1
Real number (ℝ)

Distinct946
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0678728
Minimum0.1122
Maximum4.602
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-11T12:08:42.335282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1122
5-th percentile0.156144
Q10.396475
median0.7837
Q31.46425
95-th percentile2.9751
Maximum4.602
Range4.4898
Interquartile range (IQR)1.067775

Descriptive statistics

Standard deviation0.90276259
Coefficient of variation (CV)0.84538405
Kurtosis1.6609954
Mean1.0678728
Median Absolute Deviation (MAD)0.4608
Skewness1.4095306
Sum1067.8727
Variance0.81498029
MonotonicityNot monotonic
2023-01-11T12:08:42.402453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.151 3
 
0.3%
1.153 3
 
0.3%
0.2654 3
 
0.3%
1.646 2
 
0.2%
0.2618 2
 
0.2%
1.192 2
 
0.2%
0.9378 2
 
0.2%
1.473 2
 
0.2%
1.344 2
 
0.2%
0.4813 2
 
0.2%
Other values (936) 977
97.7%
ValueCountFrequency (%)
0.1122 1
0.1%
0.11465 1
0.1%
0.11515 1
0.1%
0.11567 1
0.1%
0.11749 1
0.1%
0.11826 1
0.1%
0.11895 1
0.1%
0.1212 1
0.1%
0.12129 1
0.1%
0.12363 1
0.1%
ValueCountFrequency (%)
4.602 1
0.1%
4.5 1
0.1%
4.461 1
0.1%
4.348 1
0.1%
4.325 1
0.1%
4.27 1
0.1%
4.17 1
0.1%
4.08 2
0.2%
3.93 1
0.1%
3.891 1
0.1%

F2
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
507 
0
493 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 507
50.7%
0 493
49.3%

Length

2023-01-11T12:08:42.451693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-11T12:08:42.494671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 507
50.7%
0 493
49.3%

Most occurring characters

ValueCountFrequency (%)
1 507
50.7%
0 493
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 507
50.7%
0 493
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 507
50.7%
0 493
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 507
50.7%
0 493
49.3%

F3
Real number (ℝ)

Distinct976
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5361.8321
Minimum-15323.44
Maximum2722.56
Zeros0
Zeros (%)0.0%
Negative998
Negative (%)99.8%
Memory size7.9 KiB
2023-01-11T12:08:42.539660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-15323.44
5-th percentile-8067.74
Q1-5523.54
median-4984.54
Q3-4731.1335
95-th percentile-4128.3
Maximum2722.56
Range18046
Interquartile range (IQR)792.4065

Descriptive statistics

Standard deviation1494.9417
Coefficient of variation (CV)-0.27881173
Kurtosis12.349799
Mean-5361.8321
Median Absolute Deviation (MAD)323.87
Skewness-2.4744724
Sum-5361832.1
Variance2234850.6
MonotonicityNot monotonic
2023-01-11T12:08:42.597972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4988.84 4
 
0.4%
-5069.24 4
 
0.4%
-5272.84 2
 
0.2%
-5217.24 2
 
0.2%
-7271.44 2
 
0.2%
-6384.84 2
 
0.2%
-5021.44 2
 
0.2%
-4323.24 2
 
0.2%
-6997.44 2
 
0.2%
-5249.04 2
 
0.2%
Other values (966) 976
97.6%
ValueCountFrequency (%)
-15323.44 1
0.1%
-15023.44 1
0.1%
-14847.44 1
0.1%
-14219.44 1
0.1%
-13353.44 1
0.1%
-13057.44 1
0.1%
-12909.44 1
0.1%
-12531.44 1
0.1%
-12337.44 1
0.1%
-12285.44 1
0.1%
ValueCountFrequency (%)
2722.56 1
0.1%
890.56 1
0.1%
-1359.44 1
0.1%
-1585.44 1
0.1%
-1835.44 1
0.1%
-2091.44 1
0.1%
-2245.44 1
0.1%
-2327.44 1
0.1%
-2335.44 1
0.1%
-2399.44 1
0.1%

F4
Real number (ℝ)

Distinct948
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-13.23004
Minimum-23.889
Maximum-10.53387
Zeros0
Zeros (%)0.0%
Negative1000
Negative (%)100.0%
Memory size7.9 KiB
2023-01-11T12:08:42.657896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-23.889
5-th percentile-18.444
Q1-14.325
median-12.41625
Q3-11.337
95-th percentile-10.672717
Maximum-10.53387
Range13.35513
Interquartile range (IQR)2.988

Descriptive statistics

Standard deviation2.5603688
Coefficient of variation (CV)-0.19352691
Kurtosis2.3902351
Mean-13.23004
Median Absolute Deviation (MAD)1.3215
Skewness-1.5342713
Sum-13230.04
Variance6.5554884
MonotonicityNot monotonic
2023-01-11T12:08:42.719725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-14.787 3
 
0.3%
-15.927 3
 
0.3%
-14.529 3
 
0.3%
-10.965 3
 
0.3%
-13.65 2
 
0.2%
-10.872 2
 
0.2%
-14.373 2
 
0.2%
-11.8536 2
 
0.2%
-13.965 2
 
0.2%
-11.1288 2
 
0.2%
Other values (938) 976
97.6%
ValueCountFrequency (%)
-23.889 1
0.1%
-23.691 1
0.1%
-23.568 1
0.1%
-23.487 1
0.1%
-23.433 1
0.1%
-23.235 1
0.1%
-23.232 1
0.1%
-23.037 1
0.1%
-22.902 1
0.1%
-22.575 1
0.1%
ValueCountFrequency (%)
-10.53387 1
0.1%
-10.53735 1
0.1%
-10.53819 1
0.1%
-10.54122 1
0.1%
-10.54329 1
0.1%
-10.54947 1
0.1%
-10.55025 1
0.1%
-10.55253 1
0.1%
-10.5558 1
0.1%
-10.55643 1
0.1%

F5
Real number (ℝ)

Distinct943
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.3137924
Minimum-14.613
Maximum-3.9917757
Zeros0
Zeros (%)0.0%
Negative1000
Negative (%)100.0%
Memory size7.9 KiB
2023-01-11T12:08:42.781354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-14.613
5-th percentile-9.7902
Q1-7.4535
median-5.91225
Q3-4.8531
95-th percentile-4.163331
Maximum-3.9917757
Range10.621224
Interquartile range (IQR)2.6004

Descriptive statistics

Standard deviation1.8425346
Coefficient of variation (CV)-0.29182691
Kurtosis1.0654666
Mean-6.3137924
Median Absolute Deviation (MAD)1.20495
Skewness-1.0667962
Sum-6313.7924
Variance3.3949336
MonotonicityNot monotonic
2023-01-11T12:08:42.841507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7.26 3
 
0.3%
-7.662 3
 
0.3%
-8.226 3
 
0.3%
-8.025 3
 
0.3%
-7.161 2
 
0.2%
-8.079 2
 
0.2%
-6.6189 2
 
0.2%
-9.087 2
 
0.2%
-5.3856 2
 
0.2%
-5.5662 2
 
0.2%
Other values (933) 976
97.6%
ValueCountFrequency (%)
-14.613 1
0.1%
-13.956 1
0.1%
-13.563 1
0.1%
-12.867 1
0.1%
-12.822 1
0.1%
-12.609 1
0.1%
-12.507 1
0.1%
-12.09 1
0.1%
-11.988 1
0.1%
-11.955 1
0.1%
ValueCountFrequency (%)
-3.9917757 1
0.1%
-3.994527 1
0.1%
-3.999396 1
0.1%
-3.999717 1
0.1%
-4.000272 1
0.1%
-4.009545 1
0.1%
-4.010214 1
0.1%
-4.013655 1
0.1%
-4.02087 1
0.1%
-4.0212 1
0.1%

F6
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
519 
1
481 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 519
51.9%
1 481
48.1%

Length

2023-01-11T12:08:42.892415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-11T12:08:42.935311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 519
51.9%
1 481
48.1%

Most occurring characters

ValueCountFrequency (%)
0 519
51.9%
1 481
48.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 519
51.9%
1 481
48.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 519
51.9%
1 481
48.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 519
51.9%
1 481
48.1%

F7
Real number (ℝ)

Distinct942
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8905323
Minimum3.94276
Maximum12.744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-11T12:08:42.980108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.94276
5-th percentile4.04499
Q14.48715
median5.3096
Q36.8565
95-th percentile9.5979
Maximum12.744
Range8.80124
Interquartile range (IQR)2.36935

Descriptive statistics

Standard deviation1.7998375
Coefficient of variation (CV)0.30554752
Kurtosis1.4417542
Mean5.8905323
Median Absolute Deviation (MAD)1.0191
Skewness1.3092064
Sum5890.5323
Variance3.2394151
MonotonicityNot monotonic
2023-01-11T12:08:43.036819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.08 3
 
0.3%
6.28 3
 
0.3%
6.448 3
 
0.3%
4.5462 3
 
0.3%
4.2888 2
 
0.2%
4.0912 2
 
0.2%
6.746 2
 
0.2%
6.106 2
 
0.2%
8.568 2
 
0.2%
7.876 2
 
0.2%
Other values (932) 976
97.6%
ValueCountFrequency (%)
3.94276 1
0.1%
3.94416 1
0.1%
3.94542 1
0.1%
3.94728 1
0.1%
3.94908 1
0.1%
3.94918 1
0.1%
3.9494 1
0.1%
3.95082 1
0.1%
3.95128 1
0.1%
3.95256 1
0.1%
ValueCountFrequency (%)
12.744 1
0.1%
12.61 1
0.1%
12.51 1
0.1%
12.49 1
0.1%
12.358 1
0.1%
12.324 1
0.1%
12.208 1
0.1%
12.186 1
0.1%
12.05 1
0.1%
12.004 1
0.1%

F8
Real number (ℝ)

Distinct982
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-14.141708
Minimum-29.85
Maximum5.03
Zeros0
Zeros (%)0.0%
Negative997
Negative (%)99.7%
Memory size7.9 KiB
2023-01-11T12:08:43.094299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-29.85
5-th percentile-22.0077
Q1-17.20975
median-14.18005
Q3-11.06675
95-th percentile-6.03645
Maximum5.03
Range34.88
Interquartile range (IQR)6.143

Descriptive statistics

Standard deviation4.8616413
Coefficient of variation (CV)-0.34378034
Kurtosis0.35140073
Mean-14.141708
Median Absolute Deviation (MAD)3.09905
Skewness0.03887489
Sum-14141.708
Variance23.635557
MonotonicityNot monotonic
2023-01-11T12:08:43.152133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12.712 3
 
0.3%
-17.11 2
 
0.2%
-15.238 2
 
0.2%
-13.7385 2
 
0.2%
-12.875 2
 
0.2%
-15.15 2
 
0.2%
-16.837 2
 
0.2%
-16.677 2
 
0.2%
-11.533 2
 
0.2%
-12.927 2
 
0.2%
Other values (972) 979
97.9%
ValueCountFrequency (%)
-29.85 1
0.1%
-29.34 1
0.1%
-28.87 1
0.1%
-28.85 1
0.1%
-27.87 1
0.1%
-26.85 1
0.1%
-26.82 1
0.1%
-26.21 1
0.1%
-26.2 1
0.1%
-26.08 2
0.2%
ValueCountFrequency (%)
5.03 1
0.1%
1.86 1
0.1%
0.14 1
0.1%
-0.11 1
0.1%
-0.29 1
0.1%
-1.41 1
0.1%
-1.79 1
0.1%
-1.9 1
0.1%
-2.15 1
0.1%
-2.64 1
0.1%

F9
Real number (ℝ)

Distinct947
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-20.424599
Minimum-163.68
Maximum-0.124656
Zeros0
Zeros (%)0.0%
Negative1000
Negative (%)100.0%
Memory size7.9 KiB
2023-01-11T12:08:43.213891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-163.68
5-th percentile-63.872
Q1-26.025
median-14.284
Q3-6.504
95-th percentile-1.32328
Maximum-0.124656
Range163.55534
Interquartile range (IQR)19.521

Descriptive statistics

Standard deviation20.571395
Coefficient of variation (CV)-1.0071872
Kurtosis6.6137612
Mean-20.424599
Median Absolute Deviation (MAD)9.318
Skewness-2.1498379
Sum-20424.599
Variance423.18229
MonotonicityNot monotonic
2023-01-11T12:08:43.271618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-25.2 5
 
0.5%
-24.24 3
 
0.3%
-25.84 3
 
0.3%
-15.322 3
 
0.3%
-23.88 2
 
0.2%
-28.1 2
 
0.2%
-17.442 2
 
0.2%
-10.924 2
 
0.2%
-14.478 2
 
0.2%
-24.72 2
 
0.2%
Other values (937) 974
97.4%
ValueCountFrequency (%)
-163.68 1
0.1%
-140.78 1
0.1%
-126.76 1
0.1%
-115.58 1
0.1%
-114.9 1
0.1%
-112.76 1
0.1%
-111.42 1
0.1%
-103.92 1
0.1%
-103.3 1
0.1%
-99.8 1
0.1%
ValueCountFrequency (%)
-0.124656 1
0.1%
-0.135354 1
0.1%
-0.17516 1
0.1%
-0.19198 1
0.1%
-0.26496 1
0.1%
-0.26816 1
0.1%
-0.29188 1
0.1%
-0.29382 1
0.1%
-0.30424 1
0.1%
-0.30542 1
0.1%

F10
Real number (ℝ)

Distinct951
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9386083
Minimum3.00299
Maximum7.555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-11T12:08:43.332574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.00299
5-th percentile3.0468325
Q13.261825
median3.65725
Q34.307
95-th percentile5.9086
Maximum7.555
Range4.55201
Interquartile range (IQR)1.045175

Descriptive statistics

Standard deviation0.9012037
Coefficient of variation (CV)0.22881273
Kurtosis2.0990515
Mean3.9386083
Median Absolute Deviation (MAD)0.4696
Skewness1.4966614
Sum3938.6083
Variance0.81216811
MonotonicityNot monotonic
2023-01-11T12:08:43.390477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.224 3
 
0.3%
4.113 3
 
0.3%
3.2331 2
 
0.2%
3.8586 2
 
0.2%
3.4945 2
 
0.2%
4.089 2
 
0.2%
3.209 2
 
0.2%
4.339 2
 
0.2%
3.3572 2
 
0.2%
4.018 2
 
0.2%
Other values (941) 978
97.8%
ValueCountFrequency (%)
3.00299 1
0.1%
3.00491 1
0.1%
3.005 1
0.1%
3.00724 1
0.1%
3.00748 1
0.1%
3.01129 1
0.1%
3.01196 1
0.1%
3.0128 1
0.1%
3.01329 1
0.1%
3.01336 1
0.1%
ValueCountFrequency (%)
7.555 1
0.1%
7.538 1
0.1%
7.514 1
0.1%
7.363 1
0.1%
7.315 1
0.1%
7.279 1
0.1%
7.27 1
0.1%
7.089 1
0.1%
7.073 1
0.1%
7.052 1
0.1%

F11
Real number (ℝ)

Distinct983
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2659.1237
Minimum-5806.3
Maximum2905.7
Zeros0
Zeros (%)0.0%
Negative993
Negative (%)99.3%
Memory size7.9 KiB
2023-01-11T12:08:43.540421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-5806.3
5-th percentile-3065.325
Q1-2864.15
median-2778.455
Q3-2623.3
95-th percentile-1865.625
Maximum2905.7
Range8712
Interquartile range (IQR)240.85

Descriptive statistics

Standard deviation529.0181
Coefficient of variation (CV)-0.19894453
Kurtosis25.70332
Mean-2659.1237
Median Absolute Deviation (MAD)106.205
Skewness3.536176
Sum-2659123.7
Variance279860.16
MonotonicityNot monotonic
2023-01-11T12:08:43.596149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2598.6 3
 
0.3%
-2668.2 2
 
0.2%
-2539.8 2
 
0.2%
-2781.19 2
 
0.2%
-2550.7 2
 
0.2%
-2479.3 2
 
0.2%
-2718.9 2
 
0.2%
-2726.3 2
 
0.2%
-2804.62 2
 
0.2%
-2702.6 2
 
0.2%
Other values (973) 979
97.9%
ValueCountFrequency (%)
-5806.3 1
0.1%
-4614.3 1
0.1%
-4180.3 1
0.1%
-3947.3 1
0.1%
-3929.3 1
0.1%
-3831.4 1
0.1%
-3755 1
0.1%
-3494.4 1
0.1%
-3471.4 1
0.1%
-3459.2 1
0.1%
ValueCountFrequency (%)
2905.7 1
0.1%
1545.7 1
0.1%
949.7 1
0.1%
840.7 1
0.1%
192.7 1
0.1%
189.7 1
0.1%
150.7 1
0.1%
-80.3 1
0.1%
-377.3 1
0.1%
-387.3 1
0.1%

F12
Real number (ℝ)

Distinct955
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.6219168
Minimum-10.616
Maximum-1.64424
Zeros0
Zeros (%)0.0%
Negative1000
Negative (%)100.0%
Memory size7.9 KiB
2023-01-11T12:08:43.654123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-10.616
5-th percentile-7.4402
Q1-4.447
median-3.0645
Q3-2.2349
95-th percentile-1.74116
Maximum-1.64424
Range8.97176
Interquartile range (IQR)2.2121

Descriptive statistics

Standard deviation1.8055531
Coefficient of variation (CV)-0.49850761
Kurtosis1.4796005
Mean-3.6219168
Median Absolute Deviation (MAD)1.0067
Skewness-1.3276826
Sum-3621.9168
Variance3.2600219
MonotonicityNot monotonic
2023-01-11T12:08:43.709441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.524 3
 
0.3%
-6.184 3
 
0.3%
-3.912 3
 
0.3%
-3.612 3
 
0.3%
-1.8292 2
 
0.2%
-3.586 2
 
0.2%
-6.27 2
 
0.2%
-3.998 2
 
0.2%
-2.1162 2
 
0.2%
-2.684 2
 
0.2%
Other values (945) 976
97.6%
ValueCountFrequency (%)
-10.616 1
0.1%
-10.406 1
0.1%
-10.382 1
0.1%
-10.218 1
0.1%
-10.024 1
0.1%
-9.978 1
0.1%
-9.714 1
0.1%
-9.66 1
0.1%
-9.522 1
0.1%
-9.516 1
0.1%
ValueCountFrequency (%)
-1.64424 1
0.1%
-1.65018 1
0.1%
-1.6528 1
0.1%
-1.65436 1
0.1%
-1.65552 1
0.1%
-1.65566 1
0.1%
-1.65616 1
0.1%
-1.65618 1
0.1%
-1.65758 1
0.1%
-1.65838 1
0.1%

F13
Real number (ℝ)

Distinct973
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2442636
Minimum5.760001
Maximum11.578
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-11T12:08:43.766178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5.760001
5-th percentile5.7613589
Q15.803685
median5.9936
Q36.399525
95-th percentile7.7048
Maximum11.578
Range5.817999
Interquartile range (IQR)0.59584

Descriptive statistics

Standard deviation0.67193766
Coefficient of variation (CV)0.10760879
Kurtosis9.9887095
Mean6.2442636
Median Absolute Deviation (MAD)0.21622
Skewness2.6438452
Sum6244.2636
Variance0.45150022
MonotonicityNot monotonic
2023-01-11T12:08:43.821885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.9437 2
 
0.2%
6.777 2
 
0.2%
5.9817 2
 
0.2%
5.7600642 2
 
0.2%
6.2193 2
 
0.2%
6.899 2
 
0.2%
6.4308 2
 
0.2%
6.3841 2
 
0.2%
5.8716 2
 
0.2%
6.0562 2
 
0.2%
Other values (963) 980
98.0%
ValueCountFrequency (%)
5.760001 1
0.1%
5.7600044 1
0.1%
5.7600053 1
0.1%
5.7600067 1
0.1%
5.7600085 1
0.1%
5.7600161 1
0.1%
5.760025 1
0.1%
5.7600319 1
0.1%
5.7600322 1
0.1%
5.7600374 1
0.1%
ValueCountFrequency (%)
11.578 1
0.1%
10.269 1
0.1%
10.251 1
0.1%
10.206 1
0.1%
9.91 1
0.1%
9.226 1
0.1%
8.914 1
0.1%
8.9 1
0.1%
8.783 1
0.1%
8.742 1
0.1%

F14
Real number (ℝ)

Distinct980
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11630.294
Minimum-23402.76
Maximum-1190.76
Zeros0
Zeros (%)0.0%
Negative1000
Negative (%)100.0%
Memory size7.9 KiB
2023-01-11T12:08:43.881030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-23402.76
5-th percentile-13619.235
Q1-12032.46
median-11597.426
Q3-11223.66
95-th percentile-9447.15
Maximum-1190.76
Range22212
Interquartile range (IQR)808.8

Descriptive statistics

Standard deviation1534.7472
Coefficient of variation (CV)-0.13196117
Kurtosis13.581602
Mean-11630.294
Median Absolute Deviation (MAD)412.8
Skewness-0.81036306
Sum-11630294
Variance2355448.9
MonotonicityNot monotonic
2023-01-11T12:08:43.935424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11315.46 2
 
0.2%
-11722.95 2
 
0.2%
-12073.86 2
 
0.2%
-11940.66 2
 
0.2%
-11116.56 2
 
0.2%
-11922.36 2
 
0.2%
-11254.86 2
 
0.2%
-11022.36 2
 
0.2%
-11454.45 2
 
0.2%
-11922.06 2
 
0.2%
Other values (970) 980
98.0%
ValueCountFrequency (%)
-23402.76 1
0.1%
-21608.76 1
0.1%
-20276.76 1
0.1%
-19868.76 1
0.1%
-19355.76 1
0.1%
-19091.76 1
0.1%
-18677.76 1
0.1%
-18629.76 1
0.1%
-17984.76 1
0.1%
-17942.76 1
0.1%
ValueCountFrequency (%)
-1190.76 1
0.1%
-3461.76 1
0.1%
-3857.76 1
0.1%
-5834.76 1
0.1%
-5891.76 1
0.1%
-5894.76 1
0.1%
-6728.76 1
0.1%
-6836.76 1
0.1%
-6854.76 1
0.1%
-6908.76 1
0.1%

F15
Real number (ℝ)

Distinct973
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24105.732
Minimum10433.64
Maximum47423.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-11T12:08:43.990484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10433.64
5-th percentile21162.75
Q122905.3
median23486.04
Q324446.265
95-th percentile29468.79
Maximum47423.64
Range36990
Interquartile range (IQR)1540.965

Descriptive statistics

Standard deviation3111.5335
Coefficient of variation (CV)0.12907857
Kurtosis12.543579
Mean24105.732
Median Absolute Deviation (MAD)692.85
Skewness2.4183072
Sum24105732
Variance9681640.9
MonotonicityNot monotonic
2023-01-11T12:08:44.049634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25301.04 2
 
0.2%
23980.74 2
 
0.2%
22602.24 2
 
0.2%
24586.14 2
 
0.2%
23801.64 2
 
0.2%
22602.54 2
 
0.2%
24220.14 2
 
0.2%
23778.54 2
 
0.2%
23756.94 2
 
0.2%
23674.44 2
 
0.2%
Other values (963) 980
98.0%
ValueCountFrequency (%)
10433.64 1
0.1%
13034.64 1
0.1%
13115.64 1
0.1%
14885.64 1
0.1%
15419.64 1
0.1%
15746.64 1
0.1%
15818.64 1
0.1%
16157.64 1
0.1%
17105.64 1
0.1%
17450.64 1
0.1%
ValueCountFrequency (%)
47423.64 1
0.1%
44315.64 1
0.1%
42413.64 1
0.1%
42044.64 1
0.1%
40586.64 1
0.1%
39917.64 1
0.1%
39707.64 1
0.1%
39668.64 1
0.1%
39062.64 1
0.1%
38726.64 1
0.1%

F16
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
-1.4
529 
-0.4
471 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4000
Distinct characters5
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.4
2nd row-0.4
3rd row-1.4
4th row-1.4
5th row-1.4

Common Values

ValueCountFrequency (%)
-1.4 529
52.9%
-0.4 471
47.1%

Length

2023-01-11T12:08:44.104135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-11T12:08:44.146366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.4 529
52.9%
0.4 471
47.1%

Most occurring characters

ValueCountFrequency (%)
- 1000
25.0%
. 1000
25.0%
4 1000
25.0%
1 529
13.2%
0 471
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
50.0%
Dash Punctuation 1000
25.0%
Other Punctuation 1000
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1000
50.0%
1 529
26.5%
0 471
23.5%
Dash Punctuation
ValueCountFrequency (%)
- 1000
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1000
25.0%
. 1000
25.0%
4 1000
25.0%
1 529
13.2%
0 471
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1000
25.0%
. 1000
25.0%
4 1000
25.0%
1 529
13.2%
0 471
11.8%

F17
Real number (ℝ)

HIGH CORRELATION
SKEWED

Distinct975
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103949.54
Minimum96416.66
Maximum209390.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-11T12:08:44.192015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum96416.66
5-th percentile103622.22
Q1103814.59
median103852.23
Q3103890.88
95-th percentile104078.76
Maximum209390.66
Range112974
Interquartile range (IQR)76.29

Descriptive statistics

Standard deviation3376.4596
Coefficient of variation (CV)0.032481717
Kurtosis954.76301
Mean103949.54
Median Absolute Deviation (MAD)37.92
Skewness30.53867
Sum1.0394954 × 108
Variance11400479
MonotonicityNot monotonic
2023-01-11T12:08:44.248774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103819.8 2
 
0.2%
103783.54 2
 
0.2%
103876 2
 
0.2%
103858.85 2
 
0.2%
103908.48 2
 
0.2%
103837.67 2
 
0.2%
103866.4 2
 
0.2%
103853.02 2
 
0.2%
103817.44 2
 
0.2%
103853.07 2
 
0.2%
Other values (965) 980
98.0%
ValueCountFrequency (%)
96416.66 1
0.1%
99104.66 1
0.1%
99630.66 1
0.1%
100772.66 1
0.1%
101152.66 1
0.1%
101370.66 1
0.1%
101646.66 1
0.1%
101804.66 1
0.1%
101855.46 1
0.1%
102138.26 1
0.1%
ValueCountFrequency (%)
209390.66 1
0.1%
110796.66 1
0.1%
108806.66 1
0.1%
106152.66 1
0.1%
105849.46 1
0.1%
105659.06 1
0.1%
105598.86 1
0.1%
105554.46 1
0.1%
105517.26 1
0.1%
105382.06 1
0.1%

F18
Real number (ℝ)

Distinct936
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9887168
Minimum2.14444
Maximum11.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-11T12:08:44.308636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.14444
5-th percentile2.25049
Q12.68665
median3.4781
Q34.759
95-th percentile7.7287
Maximum11.17
Range9.02556
Interquartile range (IQR)2.07235

Descriptive statistics

Standard deviation1.7393566
Coefficient of variation (CV)0.4360692
Kurtosis2.165915
Mean3.9887168
Median Absolute Deviation (MAD)0.942
Skewness1.483233
Sum3988.7168
Variance3.0253613
MonotonicityNot monotonic
2023-01-11T12:08:44.363607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.426 3
 
0.3%
6.328 3
 
0.3%
5.09 3
 
0.3%
3.5846 2
 
0.2%
3.6872 2
 
0.2%
4.246 2
 
0.2%
2.19976 2
 
0.2%
4.456 2
 
0.2%
3.1518 2
 
0.2%
7.728 2
 
0.2%
Other values (926) 977
97.7%
ValueCountFrequency (%)
2.14444 1
0.1%
2.14674 1
0.1%
2.14974 1
0.1%
2.15266 1
0.1%
2.15268 1
0.1%
2.1534 1
0.1%
2.15378 1
0.1%
2.15492 1
0.1%
2.1561 1
0.1%
2.15786 1
0.1%
ValueCountFrequency (%)
11.17 1
0.1%
10.994 1
0.1%
10.928 1
0.1%
10.716 1
0.1%
10.71 1
0.1%
10.556 1
0.1%
10.476 1
0.1%
10.246 1
0.1%
10.166 1
0.1%
9.88 1
0.1%

F19
Real number (ℝ)

Distinct979
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1451.0536
Minimum-139.08
Maximum3091.92
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)0.4%
Memory size7.9 KiB
2023-01-11T12:08:44.420434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-139.08
5-th percentile1046.61
Q11437.5825
median1504.89
Q31537.9612
95-th percentile1674.115
Maximum3091.92
Range3231
Interquartile range (IQR)100.37875

Descriptive statistics

Standard deviation246.53872
Coefficient of variation (CV)0.16990325
Kurtosis15.54994
Mean1451.0536
Median Absolute Deviation (MAD)44.985
Skewness-2.2939132
Sum1451053.6
Variance60781.339
MonotonicityNot monotonic
2023-01-11T12:08:44.477719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1354.72 3
 
0.3%
1482.68 3
 
0.3%
1550.6 2
 
0.2%
1251.52 2
 
0.2%
1504.89 2
 
0.2%
1515.22 2
 
0.2%
1521.88 2
 
0.2%
1524.915 2
 
0.2%
1513.84 2
 
0.2%
1400.42 2
 
0.2%
Other values (969) 978
97.8%
ValueCountFrequency (%)
-139.08 1
0.1%
-114.08 1
0.1%
-113.08 1
0.1%
-50.08 1
0.1%
110.92 1
0.1%
179.92 1
0.1%
183.92 1
0.1%
195.92 1
0.1%
208.92 1
0.1%
238.92 1
0.1%
ValueCountFrequency (%)
3091.92 1
0.1%
2942.92 1
0.1%
2298.92 1
0.1%
2271.82 1
0.1%
2078.32 1
0.1%
1947.52 1
0.1%
1918.92 1
0.1%
1869.92 1
0.1%
1838.02 1
0.1%
1833.42 1
0.1%

F20
Real number (ℝ)

Distinct974
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3933.397
Minimum-11393.48
Maximum5696.52
Zeros0
Zeros (%)0.0%
Negative978
Negative (%)97.8%
Memory size7.9 KiB
2023-01-11T12:08:44.538447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-11393.48
5-th percentile-5350.07
Q1-4495.635
median-4210.1
Q3-3746.73
95-th percentile-1446.18
Maximum5696.52
Range17090
Interquartile range (IQR)748.905

Descriptive statistics

Standard deviation1405.7817
Coefficient of variation (CV)-0.35739635
Kurtosis12.424351
Mean-3933.397
Median Absolute Deviation (MAD)337.85
Skewness2.2627157
Sum-3933397
Variance1976222.3
MonotonicityNot monotonic
2023-01-11T12:08:44.594242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4122.08 2
 
0.2%
-4557.68 2
 
0.2%
-3948.88 2
 
0.2%
-4040.28 2
 
0.2%
-3957.68 2
 
0.2%
-3852.68 2
 
0.2%
-3926.28 2
 
0.2%
-3483.08 2
 
0.2%
-4460.48 2
 
0.2%
-4010.48 2
 
0.2%
Other values (964) 980
98.0%
ValueCountFrequency (%)
-11393.48 1
0.1%
-9539.48 1
0.1%
-8561.48 1
0.1%
-8507.48 1
0.1%
-7415.48 1
0.1%
-7355.48 1
0.1%
-7245.48 1
0.1%
-7065.48 1
0.1%
-6973.48 1
0.1%
-6893.48 1
0.1%
ValueCountFrequency (%)
5696.52 1
0.1%
5620.52 1
0.1%
5252.52 1
0.1%
5134.52 1
0.1%
3600.52 1
0.1%
2886.52 1
0.1%
2702.52 1
0.1%
2170.52 1
0.1%
2144.52 1
0.1%
2068.52 1
0.1%

F21
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct294
Distinct (%)58.8%
Missing500
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean-44.65056
Minimum-54.96
Maximum-34.53
Zeros0
Zeros (%)0.0%
Negative500
Negative (%)50.0%
Memory size7.9 KiB
2023-01-11T12:08:44.654763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-54.96
5-th percentile-50.0145
Q1-46.875
median-44.505
Q3-42.45
95-th percentile-39.411
Maximum-34.53
Range20.43
Interquartile range (IQR)4.425

Descriptive statistics

Standard deviation3.188244
Coefficient of variation (CV)-0.071404344
Kurtosis-0.16792998
Mean-44.65056
Median Absolute Deviation (MAD)2.175
Skewness-0.05826932
Sum-22325.28
Variance10.164899
MonotonicityNot monotonic
2023-01-11T12:08:44.714041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-45.15 8
 
0.8%
-43.41 6
 
0.6%
-44.04 5
 
0.5%
-44.1 5
 
0.5%
-44.46 4
 
0.4%
-45.39 4
 
0.4%
-42.09 4
 
0.4%
-48.27 4
 
0.4%
-46.11 4
 
0.4%
-45.57 4
 
0.4%
Other values (284) 452
45.2%
(Missing) 500
50.0%
ValueCountFrequency (%)
-54.96 1
0.1%
-53.19 1
0.1%
-52.98 1
0.1%
-52.89 1
0.1%
-51.75 1
0.1%
-51.66 1
0.1%
-51.57 1
0.1%
-51.54 1
0.1%
-51.42 1
0.1%
-51.12 1
0.1%
ValueCountFrequency (%)
-34.53 1
0.1%
-37.2 1
0.1%
-37.59 1
0.1%
-37.62 1
0.1%
-37.65 1
0.1%
-37.86 2
0.2%
-38.16 1
0.1%
-38.22 1
0.1%
-38.4 1
0.1%
-38.49 1
0.1%

Class
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
506 
False
494 
ValueCountFrequency (%)
True 506
50.6%
False 494
49.4%
2023-01-11T12:08:44.770204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Interactions

2023-01-11T12:08:41.065302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:24.616274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.519956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.499573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.559585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.486704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.397734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.459599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.441022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.345177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.353184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.270186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.198048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:36.200901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.226571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.170708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.078055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.112862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.121949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:24.664534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.572246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.551417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.609296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.537105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.447514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.512740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.488257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.393630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.400371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.318884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.245000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:36.256520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.276767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.217206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.128909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.164218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.172839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:24.716478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.628949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.609235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.663279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.593458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.504704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.570668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.542559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.446737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.454775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.374063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.298895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:36.319239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.330945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.273223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.183839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.218608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.226457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:24.771312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.688551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.664375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.717603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.646731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.561815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.630329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.595547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.500398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.507639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.430540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.352048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:36.377338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.385904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.326073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.330605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.275316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.274566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:24.820187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.744465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.717066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.768238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.697048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.613629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.684870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.645866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.549565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.558347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.481663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.402550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:36.437194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.436946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.376263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.382397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.327125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.320902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:24.866392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.796777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.768232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.818164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.743086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.665271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.736187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.692138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.598241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.605990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.529787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.450182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:36.499962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.486972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.423074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.433889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.377851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.372918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:24.918101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.855960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.920363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.872010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.795781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.720986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.794841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.745430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.652341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.659670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.584144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.501810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:36.559137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.542634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.476522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.488260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.431842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.426838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:24.972623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.914187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.977301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.927232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.850897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.780784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.852678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.799420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.707895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.714149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.640074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.557388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:36.621914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.603277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.530882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.545222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.489572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.475051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.020894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.966083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.030099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.975909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.899596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.831989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.904308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.847631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.754999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.761511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.688529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.605554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:36.682394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.653419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.578631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.594859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.539268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.526685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.069309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.016700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.080734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.024639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.947417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.884991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.956727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.894890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.801699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.810650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.736215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.655390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:36.739669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.703132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.625845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.645596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.590590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.573640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.117087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.069412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.131739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.073855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.994293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.029824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.009515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.944817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.849475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.858256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.785214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.702850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:36.792304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.754581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.674116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.695242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.639986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.620376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.166438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.122638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.183050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.125521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.045437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.083190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.062426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.995726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.899024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.908190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.835350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.753647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:36.847837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.805369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.724681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.747160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.693893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.673794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.213165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.172641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.233412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.173557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.091451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.134049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.113084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.041895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.042958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.954762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.883660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.798249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:36.898838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.853270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.770514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.796736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.741982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.722388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.266381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.229842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.289911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.228979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.146241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.191868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.170135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.096035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.096621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.009722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.938196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.850561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:36.955192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.908659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.826366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.853508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.798352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.774365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.316103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.284865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.344034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.281448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.194398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.244998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.224474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.145827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.147147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.060163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.990968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.903001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.009301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.961392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.877657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.905690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.850812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.821350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.362536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.335427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.393883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.329912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.242723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.295619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.275125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.192892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.194876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.111882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.040130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.950822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.061598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.011311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.925614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.955863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.902708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.871712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.415139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.390541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.450124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.383094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.294123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.350058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.330699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.244189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.247781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.165070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.093533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:36.003784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.116569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.065223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.976945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.008501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.956858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.924238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:25.466887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:26.445078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:27.506102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:28.434661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:29.347530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:30.406118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:31.386150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:32.296438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:33.298461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:34.219044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:35.145429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:36.054098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:37.174404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:38.117412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:39.028307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:40.062153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:08:41.011951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-11T12:08:44.908443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2023-01-11T12:08:45.014966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-11T12:08:45.120706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-11T12:08:45.228039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-11T12:08:45.318820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-01-11T12:08:45.388296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-11T12:08:42.106807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-11T12:08:42.243150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

F1F2F3F4F5F6F7F8F9F10F11F12F13F14F15F16F17F18F19F20F21Class
01.64300-4894.24-13.0281-4.79340005.1270-17.1100-63.3403.61690-2671.50-2.360005.783440-11315.4622912.53-0.4103811.345.438001747.920-4879.68-41.58False
10.53100-5085.44-16.2210-3.99177604.6256-4.5800-10.3143.64880-2826.59-5.968008.180000-12852.9625696.44-0.4103884.025.096001496.080-4186.38-45.96True
20.26400-7021.44-11.7591-6.16170004.3628-14.7118-6.8063.62830-2490.50-3.391405.760312-11012.1620232.84-1.4103987.082.365201523.412-4067.28NaNFalse
30.31961-4648.76-11.8110-4.21770008.9380-7.5360-4.6703.01503-2472.00-2.679606.437100-10297.8623592.84-1.4103842.084.408001506.8101352.52NaNTrue
44.08000-4877.20-11.2635-8.06100016.2800-14.5805-45.9203.60030-2405.50-5.006006.393200-11527.3824778.74-1.4103842.483.133401581.790-5095.88-45.93True
50.45680-4983.04-14.7090-6.64020014.3992-15.2480-16.4586.59600-2725.50-2.629606.419900-12340.8622063.44-0.4103927.246.248001387.720-4226.80-43.56False
60.26200-2091.44-13.4490-4.80060016.8580-10.0970-8.4087.08900-2883.31-5.688005.771680-9447.9623027.52-0.4103842.302.228681525.276-4274.68-34.53False
70.81440-8513.44-10.7589-10.09800005.5288-15.2360-12.3183.15760-1589.30-1.656186.536600-12202.8624397.44-1.4103911.162.425801308.320-5450.68-43.50True
81.11200-4622.90-17.6280-8.295000110.1540-11.0030-7.9843.39890-2764.36-1.723545.805580-10919.4622727.94-1.4103972.143.719001716.520-3306.68NaNTrue
92.25701-5003.44-18.4350-4.79970014.0732-14.5573-14.6263.14900-2778.49-2.119406.878000-11922.6616157.64-0.4103799.387.27800979.120-93.48NaNTrue
F1F2F3F4F5F6F7F8F9F10F11F12F13F14F15F16F17F18F19F20F21Class
9901.50100-4364.640-12.9276-6.425114.21140-7.0080-7.97403.8131-2852.08-3.47426.038300-11398.4128691.64-1.4103804.843.85521489.930-3842.28-50.22False
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